Hype is often exaggerated. This is especially true for industries and sectors in flux, including the financial services industry which has seen a massive realignment as technology redefines and reimagines traditional business and services.

This dynamism has created a hype around a multitude of new technologies: Blockchain, open API, AI, augmented reality, and Internet of Things, many of which are at nascent stages of development. This hype is driving massive changes by bringing in a new generation of financial products and services, which are customized for the digital native consumer. As an example, large multinational banks today are setting up innovation incubators and organizing hackathons with specialized teams to focus on Blockchain, seen as the next-generation disruptive force that will present multifarious opportunities, new and existing, that will likely overhaul existing banking infrastructure, speed up settlements, streamline stock exchanges, and herald in a new generation of businesses and consumers into the banking system.

As shrinking revenues, increased regulatory and political pressures, heightened operational costs, and changing customer habits threaten bottom lines, banks and financial institutions are continuously working to find newer avenues to reach out to customers by offering new products and services that cater to the digital native customer. Even here, there are fresh challenges as Fintech start-ups and global technology titans are setting up newer, complementary business models, causing major disruptions and challenging traditional banking business models. Add to this the pressure from regulators across the world, who are calling for stricter compliance norms and stronger financial discipline. It is in these times of flux that banks are waking up to a potential that seem to justify the hype.

The convergence of machine and human intelligence is disrupting traditional decision-making by equipping organizations with knowledge and insight to predict and prescribe business outcomes. Advances in Big Data and analytics have led to new products, solutions, and services, making financial institutions smarter, agile, and more competitive. Newer regulatory and compliance requirements, fraud, and anti-money laundering preventive steps are placing more emphasis on stronger governance and risk management. Data security and data protection is gaining significance. This is driving up operating expenses, necessitating financial institutions to explore avenues to improve operational efficiencies.

In the past decade or so, Big Data and analytics have reshaped and reimagined businesses. While banks are investing in next-generation infrastructure solutions and quantitative approaches, they understand that ROI and value generation is a long-term solution and investments in Big Data need to be intelligently spaced out in order to gain maximum benefit. This is not only demonstrated by discussions from management consulting reports, but also by changes in the manner in which CIO and CDO positions are looked at in many banks as these financial executives have been progressively scrutinized after several years of high spending. The hype has meant that banks are wringing costs from legacy modernization and storage, and are offloading analytic workloads and utilizing “next-generation” Big Data solutions. Some of the biggest trends these days is adoption of cloud computing — first, private cloud and increasingly, hybrid cloud environments.

As the 2016 Wells Fargo account scandal highlighted, the stakes are now raised for fraud each year , and the fines are climbing; sanctions and compliance demands have forced banks to increase their transaction monitoring, KYC compliance and money laundering detection and prevention efforts. Regulatory agencies will also increase their scrutiny of business practices and investigation of potential financial crimes. As financial data governance improves, the debate is shifting toward how banks are utilizing their Big Data and analytics platforms to integrate historical and real-time financial data sets. Deeper internal skills sets are being deployed to help apply predictive techniques that enhance existing human and descriptive analytical efforts.

We are entering a critical phase for vendors and financial firms as they look to partner and ensure that large deployments are successful. This will usher in more opportunity to impact business productivity, and increase the pervasiveness and value of Big Data. As we move ahead, we will see continued growth in core use cases for the financial sector, including not only the IT-focused use cases like warehouse offload, storage, and reporting, but also business applications such as risk and marketing. Fundamentals are the focus of Big Data projects. Most banks, wealth management firms, asset managers, and insurance firms are non-digital natives, so the process of conversion is longer. Big Data platforms require a fair amount of behavioral change — tools, functions, quality, and usage need agreement across business and technology groups. There is a need to reorient businesses for this change.

As the hype continues, it is important to remember that security for financials should be at the forefront of Big Data efforts to enable the expansion of new projects. If data is the new currency of banks, they must accordingly treat it as a monetary asset. Finally, while the pool of next-generation data science and developers are growing, banks still need to aggressively compete for talent. The supply of this type of talent thins even more as you go down market. While Big Data adoption in financials is still at a relatively early stage, it is important to note that banks, insurers, and asset management firms represent some of the most advanced Big Data users in the world. However, the situation of haves and have-nots will likely close over time.